A blog post discusses the challenges of evaluating changes in AI model performance, particularly when both the model and the evaluation instrument (an LLM judge) are updated simultaneously. The author advocates for using the Bland-Altman method, originally from clinical measurement, to assess agreement between two instruments rather than simple correlation. This method involves plotting the difference between scores against their average, which helps identify offsets and understand the uncertainty associated with these measurements. The post details how to calculate confidence intervals for offsets and limits of agreement, emphasizing that the precision of these corrections is crucial when interpreting performance shifts. AI
IMPACT Provides a statistical framework for more robust evaluation of LLM performance changes.
RANK_REASON Blog post discussing statistical methods for evaluating LLM performance.
- Bland and Altman
- judge
- Statistical methods for assessing agreement between two methods of clinical measurement
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